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We introduce CryptGPU, a system for privacy-preserving machine learning that implements all operations on the GPU (graphics processing unit).
A. C. Yao, “How to generate and exchange secrets (extended abstract),” in FOCS
1986
Earlier work this paper cites.
O. Goldreich, S. Micali, and A. Wigderson, “How to play any mental game or A completeness theorem for protocols with honest majority,” in STOC
1987
Earlier work this paper cites.
M. Ben-Or, S. Goldwasser, and A. Wigderson, “Completeness theorems for non-cryptographic fault-tolerant distributed computation (extended abstract),” in STOC
1988
Earlier work this paper cites.
Y. LeCun, B. E. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. E. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural Comput
1989
Earlier work this paper cites.
M. Ito, A. Saito, and T. Nishizeki, “Secret sharing scheme realizing general access structure,” Electronics and Communications in Japan (Part III: Fundamental Electronic Science)
1989
Earlier work this paper cites.
D. Beaver, “Efficient multiparty protocols using circuit randomization,” in CRYPTO
1991
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE
1998
Earlier work this paper cites.
R. Canetti, “Security and composition of multiparty cryptographic protocols,” J. Cryptol
2000
Earlier work this paper cites.
Cambridge University Press, 2004
O. Goldreich, The Foundations of Cryptography - Volume 2: Basic Applications · 2004
Earlier work this paper cites.
K. Chellapilla, S. Puri, and P. Simard, “High performance convolutional neural networks for document processing,” 2006
2006
Earlier work this paper cites.
A. Krizhevsky, “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
PhD thesis, Stanford University, 2009
C. Gentry, A fully homomorphic encryption scheme · 2009
Earlier work this paper cites.
D. C. Ciresan, U. Meier, L. M. Gambardella, and J. Schmidhuber, “Deep, big, simple neural nets for handwritten digit recognition,” Neural Comput
2010
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in ICML
2010
Earlier work this paper cites.
S. Kamara, P. Mohassel, and M. Raykova, “Outsourcing multi-party computation,” IACR Cryptol. ePrint Arch
2011
Earlier work this paper cites.
J. K. Salmon, M. A. Moraes, R. O. Dror, and D. E. Shaw, “Parallel random numbers: as easy as 1, 2, 3,” in Conference on High Performance Computing Networking, Storage and Analysis, SC
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in NeurIPS
2012
Earlier work this paper cites.
R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in ACM CCS
2015
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. S. Bernstein, A. C. Berg, and F. Li, “Imagenet large scale visual recognition challenge,” Int. J. Comput. Vis
2015
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in ICLR
2015
Cited alongside, same era.
D. Demmler, T. Schneider, and M. Zohner, “ABY - A framework for efficient mixed-protocol secure two-party computation,” in NDSS
2015
Cited alongside, same era.
G. Ateniese, L. V. Mancini, A. Spognardi, A. Villani, D. Vitali, and G. Felici, “Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers,” Int. J. Secur. Networks
2015
Cited alongside, same era.
M. Fredrikson, S. Jha, and T. Ristenpart, “Model inversion attacks that exploit confidence information and basic countermeasures,” in ACM CCS
2015
Cited alongside, same era.
M. Abadi, A. Chu, I. J. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in ACM CCS
2016
C. Juvekar, V. Vaikuntanathan, and A. Chandrakasan, “GAZELLE: A low latency framework for secure neural network inference,” in USENIX Security Symposium
2018
Later among the works it cites.
A. A. Badawi, B. Veeravalli, C. F. Mun, and K. M. M. Aung, “High-performance FV somewhat homomorphic encryption on gpus: An implementation using CUDA,” IACR Trans. Cryptogr. Hardw. Embed. Syst
2018
Later among the works it cites.
B. D. Rouhani, M. S. Riazi, and F. Koushanfar, “DeepSecure: Scalable provably-secure deep learning,” in Annual Design Automation Conference
2018
Later among the works it cites.
S. Wagh, D. Gupta, and N. Chandran, “SecureNN: 3-party secure computation for neural network training,” Proc. Priv. Enhancing Technol
2019
Later among the works it cites.
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Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR
2016
Cited alongside, same era.
2016
Cited alongside, same era.
T. Araki, J. Furukawa, Y. Lindell, A. Nof, and K. Ohara, “High-throughput semi-honest secure three-party computation with an honest majority,” in ACM CCS
2016
Cited alongside, same era.
MIT Press, 2016
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning · 2016
Cited alongside, same era.
R. Gilad-Bachrach, N. Dowlin, K. Laine, K. E. Lauter, M. Naehrig, and J. Wernsing, “Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy,” in ICML
2016
Cited alongside, same era.
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart, “Stealing machine learning models via prediction apis,” in USENIX Security Symposium
2016
Cited alongside, same era.
P. Mohassel and Y. Zhang, “SecureML: A system for scalable privacy-preserving machine learning,” in IEEE Symposium on Security and Privacy
2017
Cited alongside, same era.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: An imperative style, high-performance deep learning library,” in NeurIPS
2019
Later among the works it cites.
H. Chaudhari, A. Choudhury, A. Patra, and A. Suresh, “ASTRA: high throughput 3pc over rings with application to secure prediction,” in ACM CCS
2019
Later among the works it cites.
F. Tramèr and D. Boneh, “Slalom: Fast, verifiable and private execution of neural networks in trusted hardware,” in ICLR
2019
Later among the works it cites.
M. S. Riazi, M. Samragh, H. Chen, K. Laine, K. E. Lauter, and F. Koushanfar, “XONN: xnor-based oblivious deep neural network inference,” in USENIX Security Symposium
2019
Later among the works it cites.
2019
Later among the works it cites.
P. Mishra, R. Lehmkuhl, A. Srinivasan, W. Zheng, and R. A. Popa, “Delphi: A cryptographic inference service for neural networks,” in USENIX Security
2020
Later among the works it cites.
N. Kumar, M. Rathee, N. Chandran, D. Gupta, A. Rastogi, and R. Sharma, “CrypTFlow: Secure tensorflow inference,” in IEEE Symposium on Security and Privacy
2020
Later among the works it cites.
B. Knott, S. Venkataraman, A. Hannun, S. Sengupta, M. Ibrahim, and L. van der Maaten, “CrypTen: Secure multi-party computation meets machine learning,” in Proceedings of the NeurIPS Workshop on Privacy-Preserving Machine Learning
2020
Later among the works it cites.
A. Patra and A. Suresh, “BLAZE: blazing fast privacy-preserving machine learning,” in NDSS
2020
Later among the works it cites.
A. P. K. Dalskov, D. Escudero, and M. Keller, “Secure evaluation of quantized neural networks,” Proc. Priv. Enhancing Technol
2020
Later among the works it cites.
M. Byali, H. Chaudhari, A. Patra, and A. Suresh, “FLASH: fast and robust framework for privacy-preserving machine learning,” Proc. Priv. Enhancing Technol
2020
Later among the works it cites.
H. Chaudhari, R. Rachuri, and A. Suresh, “Trident: Efficient 4pc framework for privacy preserving machine learning,” in NDSS
2020
Later among the works it cites.
A. A. Badawi, J. Chao, J. Lin, C. F. Mun, S. J. Jie, B. H. M. Tan, X. Nan, A. M. M. Khin, and V. Chandrasekhar, “Towards the alexnet moment for homomorphic encryption: HCNN, the first homomorphic cnn on encrypted data with gpus,” IEEE Transactions on Emerging Topics in Computing
2020
Later among the works it cites.
S. Wagh, S. Tople, F. Benhamouda, E. Kushilevitz, P. Mittal, and T. Rabin, “FALCON: honest-majority maliciously secure framework for private deep learning,” Proc. Priv. Enhancing Technol
2021
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